See DeskFerry qualify leads
A live look at the agent doing the work — not a feature list.
New lead from Northstar just signed up — score them.
Morgan Chen
VP Ops · Northstar · pricing form
ICP fit
Built in plain English.
You write the rule the way you'd describe it to a teammate. The agent reads the rule, breaks it into the actions it'll take, and confirms the apps it'll touch — before it does anything.
- 1Enrich from LinkedIn, firmographic and tech-stack sources
- 2Score fit on funding, team size, role and buying intent
- 3Attach the reasoning and push to the CRM
- 4Route strong matches to the right rep; filter the rest
Qualify Leads Automatically: workflows DeskFerry runs
Score every new lead against your ICP
A lead is created in your CRM or fills a form
Enrich from LinkedIn, firmographic and tech-stack sources
Score fit on funding, team size, role and buying intent
Attach the reasoning and push to the CRM
Route strong matches to the right rep; filter the rest
Reps see only qualified leads, with the why attached — ~6 hrs/week reclaimed.
Re-score on new signals
A known lead shows fresh intent (pricing visit, demo request)
Re-pull enrichment and recent activity
Recompute the ICP score and flag the change
Alert the owner if the lead crosses the hot threshold
Warming leads get surfaced the moment they're ready, not days later.
Who it's for
SDR / BDR
Hours wasted researching and disqualifying leads that were never a fit.
Only worked leads that already passed an ICP check with the reasoning shown.
RevOps lead
A static points table goes stale and reps don't trust the scores.
Scoring that adapts as you close, with transparent, auditable reasoning.
For Qualify Leads Automatically: DeskFerry vs Relevance AI
| Aspect | Relevance AI | DeskFerry |
|---|---|---|
| Time to first leads | Hours (some technical knowledge needed) | 30 minutes or less |
| Technical skills required | Some technical knowledge | None - plain English setup |
| Intelligence level | AI tools (low-code) | AI that understands context |
| Handles edge cases | Manual configuration updates | Adapts automatically |
| Lead context awareness | Limited to data in workflow | Full context from all connected tools |
The real cost of leads
Monthly Cost
Hidden costs with Relevance AI:
- Developer time for advanced configurations
- Training costs for technical features
- Learning curve for low-code interface
- Time spent troubleshooting leads issues
Time to Results
Setup Time
First Results
Why DeskFerry is better for leads
ICP 87/100 · Hot
Prepared and waiting on your approval
Outside the agent's confidence — your call
Approve before it sends.
Every draft lands in a review queue. You approve, edit, or reject — the agent never acts on its own unless you explicitly turn that on for a workflow you trust.
Every action, with the reasoning attached.
Each step the agent takes is logged with what it did, why it did it, and which app it touched. Audit-ready, so security and compliance can sign off without backfilling.
- Youjust now
Approved — route to sales
- Agent1 min ago
Held for human review
Reason: Borderline ICP score — surfacing for your call before routing.
- Agent1 min ago
Scored the lead against your ICP
- Agent2 min ago
Pulled context from LinkedIn, HubSpot and Salesforce
- Agent2 min ago
Triggered: New lead from Northstar just signed up — score them.
Common questions
DeskFerry vs Relevance AI for leads
Keep exploring
Full DeskFerry vs Relevance AI comparison
This page is about leads. The full comparison covers pricing, security, and every feature head-to-head.
More DeskFerry vs Relevance AI use cases
Ready to qualify leads the easy way?
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